{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier with Randomized Search\n",
    "Decision tree is trained on feature set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 16) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 16) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_train_std.shape, \"and labels: \", y_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_test_std), X_test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train decision tree classifier - randomized search for parameter optimization\n",
      "Randomized search took 0.00 minutes \n",
      "   \n",
      "Result of randomized search:\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=17,\n",
      "            max_features=None, max_leaf_nodes=132,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=3, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=42, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "params = {'max_depth': list(range(9,18)),'max_leaf_nodes': list(range(50, 150)), 'min_samples_split': [2,3,4],\n",
    "          'max_features': [None,'auto','sqrt','log2'], 'criterion':['gini', 'entropy']}\n",
    "\n",
    "rand_search_cv = RandomizedSearchCV(DecisionTreeClassifier(random_state=42), params, verbose=0)\n",
    "\n",
    "start = time()\n",
    "print(\"Train decision tree classifier - randomized search for parameter optimization\")\n",
    "rand_search_cv.fit(X_train_std, y_train)\n",
    "print(\"Randomized search took %.2f minutes \"%((time() - start)//60))\n",
    "print(\"   \")\n",
    "print(\"Result of randomized search:\")\n",
    "print(rand_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "Decision Tree's accuracy on -20 dB SNR samples =  0.12475\n",
      "Decision Tree's accuracy on -18 dB SNR samples =  0.12625\n",
      "Decision Tree's accuracy on -16 dB SNR samples =  0.1295\n",
      "Decision Tree's accuracy on -14 dB SNR samples =  0.12975\n",
      "Decision Tree's accuracy on -12 dB SNR samples =  0.147\n",
      "Decision Tree's accuracy on -10 dB SNR samples =  0.166\n",
      "Decision Tree's accuracy on -8 dB SNR samples =  0.24925\n",
      "Decision Tree's accuracy on -6 dB SNR samples =  0.328\n",
      "Decision Tree's accuracy on -4 dB SNR samples =  0.40975\n",
      "Decision Tree's accuracy on -2 dB SNR samples =  0.42925\n",
      "Decision Tree's accuracy on 0 dB SNR samples =  0.48025\n",
      "Decision Tree's accuracy on 2 dB SNR samples =  0.61775\n",
      "Decision Tree's accuracy on 4 dB SNR samples =  0.74725\n",
      "Decision Tree's accuracy on 6 dB SNR samples =  0.79525\n",
      "Decision Tree's accuracy on 8 dB SNR samples =  0.809\n",
      "Decision Tree's accuracy on 10 dB SNR samples =  0.821\n",
      "Decision Tree's accuracy on 12 dB SNR samples =  0.8285\n",
      "Decision Tree's accuracy on 14 dB SNR samples =  0.8255\n",
      "Decision Tree's accuracy on 16 dB SNR samples =  0.81225\n",
      "Decision Tree's accuracy on 18 dB SNR samples =  0.81725\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = rand_search_cv.predict(X_test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_test[snr], y_pred[snr])\n",
    "    print(\"Decision Tree's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArkAAAGcCAYAAADd17isAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x246b0bc3860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    436     0      0     0     0     43     35     4\n",
      "BPSK      1   482      0     0    11      3      6     2\n",
      "CPFSK    12     0    461    15     0      3      5     0\n",
      "GFSK     10     1     39   485     1     10     11     3\n",
      "PAM4      2    13      0     0   485      8      9     3\n",
      "QAM16     6     0      0     0     0    218    201     4\n",
      "QAM64     6     0      0     0     0    190    221     3\n",
      "QPSK     27     4      0     0     3     25     12   481\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = rand_search_cv.predict(X_test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.84  0.00   0.00  0.00  0.00   0.08   0.07  0.01\n",
      "BPSK   0.00  0.95   0.00  0.00  0.02   0.01   0.01  0.00\n",
      "CPFSK  0.02  0.00   0.93  0.03  0.00   0.01   0.01  0.00\n",
      "GFSK   0.02  0.00   0.07  0.87  0.00   0.02   0.02  0.01\n",
      "PAM4   0.00  0.02   0.00  0.00  0.93   0.02   0.02  0.01\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.51   0.47  0.01\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.45   0.53  0.01\n",
      "QPSK   0.05  0.01   0.00  0.00  0.01   0.05   0.02  0.87\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcUAAAGFCAYAAACMmejoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XmcXEW9/vHPk5gAYYlANJFFAWXzh0DCJoKg7IggeCMZ\nQEFAEI1XDQSXixhAEcHAVVkErhDIFUzCcgUUCIug7EgS9oQ17BAIYAIkIdv390edDj2d7pnT0z3T\nPTPPm9d5zUydOnXqzIT5TtWpRRGBmZmZQZ9GV8DMzKxZOCiamZllHBTNzMwyDopmZmYZB0UzM7OM\ng6KZmVnGQdHMzCzjoGhmZpZxUDQzM8s4KFqvI+lTkm6S9G9JSyTtV+fyPyFpqaRD61ludybpdkm3\nNboeZu1xULSGkLSBpAskPSNpvqQ5ku6U9H1JK3by7ccD/w/4L+AbwAOdcI+GrJ8oaVwWkP8taYUy\n5z+VnV8q6dgOlP8xSWMkbV7lpQEsrfZ+Zl3tQ42ugPU+kvYBJgELSAHqUaA/sCNwBvBp4JhOuveK\nwGeBX0TEeZ1xj4h4XtJKwKLOKD+HxcAAYF/gypJzh5C+78sFzJzWAsYAM4GHq7hu9w7ez6xLuaVo\nXUrSesCfSb9UN42IURFxUUT8ISIOIQXExzqxCh/NPs7pxHsQEQujcavtLwBuBQ4qc+5g4K81lK2q\nMqc/DoiIxRGxuIb7mnUJB0Xraj8GVgaOjIjXS09GxLMRcXbha0l9JZ0o6WlJCyTNlHSqpP7F10l6\nTtK1knaQdF/WJfuMpG8U5RkDPEfqyhubdSE+m527RNLM0vpIOknS0pK03SXdIeltSe9ImiHp1KLz\nZd8pStolu+7d7Nq/SNqk3P0kfTKr09tZV+jFVXYrXw58SdJqRWVvA3wqO9cquElaXdJYSQ9nzzRH\n0vXF3aSSdgbuz75/l2T1XFJ4zuy94cOShkn6p6T3gFOLzv29qKxLsp/RxiX1mCzpTUlDqnhWs7px\nULSu9mXg2Yi4L2f+i4CTSe/9fgjcDvyU1NosFsCGwBXATcCxwFvAOEmbZnmuysoQKTB8Pfu6cH25\nll2rdEmfBq4D+gEnZve5BvhcWw8haTfgRmAQqfvxzOyaOyV9vOR+kLqXVwZ+AkwEDsuuy+vqrKyv\nFqUdDMwAppXJvwGwH+nZRpG6sTcDbi8KUNOBn5O+fxeQvn/fAP5ZVPdBwPXAVOAHwG1F54r9AHgD\nuFSSACR9G9gN+F5EvFbFs5rVT0T48NElB7AqabDF1Tnzb57lP78k/QxgCbBzUdrMLO1zRWmDgPnA\nGUVpn8jKPLakzHGkYF1ahzHAkqKvf5DdZ/U26l24x6FFadOAV4GBRWmfIb3/G1dyv6XAhSVlXgW8\nnuN7Ng6Ym30+Cbgp+1zAK8AJ5b4HQL8yZX08+/6dUJS2VemzFZ27LfvefKvCub+XpO2elfVTYD1g\nLnBlo/+d+ujdh1uK1pUKXXnv5Mz/JVIL479L0s8k/ZLfpyT98Yi4u/BFRMwGniC1gurl39nHAwot\nnPZkLa0tSMFv2bvMiHgEuJn0nMWC1BIrdgewpqRVqqjr5cAXJH0U2BUYnKUtJyKWDQqS1EfSGsA8\n0vdvWBX3fB+4JE/GiLiZ9JxjSC3b+XTSACuzvBwUrSvNzT6umjN/oUXzdHFiRMwiBadPlOR/oUwZ\nbwOrV1HH9kwE7gL+B5gl6c+SvtZOgCzU88ky56YDgwoDUoqUPsvb2cdqnuV60h8gLaSu039FxHLv\nTQGUjJL0JCmwzQZeJ7VmB1Zxz5ejugE1o0nd3FsA38/+kDFrGAdF6zIR8Q6pC2+zai/NmW9JhfQ8\nLbpK9+jbKlPEgojYifTuazwpaEwEbsrbcsyplmcB0ghY4P9I7yMPoEIrMXMCqQV+O2naxh6kZ3yc\n6n5PzK8iL6RWaGFE8GeqvNas7hwUrav9FfikpO1y5H2e9G90w+LErDvww9n5enk7K7PUeuUyR8Rt\nETE6IjYjBZRdgC9WKLtQz43LnNsEmB0R1QaTvC4HhgKrABPayPcfpHd+R0fEpIi4JSL+zvLfk7pN\nM5E0gPQO9DHgQuDHkraqV/lmHeGgaF3tDNK7qj9mwa2VbCrC97Mvrye1jH5Yku040i/nv9WxXs8A\nAyUta8VK+hiwf0n9ynVfPpTVs+yE+EgjKR8EDiuZIrEZqUVWz+codRvwM9KIzuWmwBRZwvLTNL4G\nrF2S773sY7k/IKp1BrAOcCjpZ/ocaTRqvzqUbdYhXtHGulREPCvpYFKrZbqk4hVtdgCGk1oPRMTD\nki4Fjs6C0T+A7Ui/RK+OiH/UsWoTgNOBv0j6PWk6xDEsP9Dk55J2IgWy50mDV75Degd4ZxvlH08K\n8vdKuoi04sz3SC3Uk+v4HK1ERAC/ypH1r8CJki4G7iZ1ZR5C+mOh2DOk97nHSHqXFCTvjYiqWu2S\ndiF938ZExENZ2uGk7ttfkuazmnU5txSty0XEdaTpFleQ5sadA/waWJ808OIHRdmPJI1O3Jo0CvUL\npAnhpau1VJpnSJn05fJGxFukVuF7pOD4DdIcwdLVX64hBcPDs3p/h/SLfNfsnWnZe0bErcBepAEs\nJ5PmN94N7FhtQMkhTxdn6ffgV6R3insAvwW2JI2KfbE4XzaI5lBSy/IPpO7ZnXPeO80NSSNoLwKm\nUBSwI+JO4HfAsZK2zfEMZnWn9IekmZmZuaVoZmaWcVA0MzPLOCiamZllHBTNzMwynpLRSSStCexJ\nmnu1oLG1MbMeYEXSYhKTI+LNBtellWynl0EduHR2RJRbnrFhHBQ7z57AZY2uhJn1OIfQ9pJ9XUrS\nx/vQ7/mlLGo/8/LmSdq0mQKjg2LneQ5g648ezKr9B3e4kIdnX8Pmg75Sc2VOv+rgmss4bvSxnDn2\nrJrL6Sn1gJ5Zl/cXdOiX2zI/+emP+PVpZ9RcD/WpfSnZH//keE7/9W9qLqdf/77tZ2rH6NHHMXbs\nmR2+fsaMGRx22KGQ/W5pIoOWsohN2J8BVTQW5zGbGfxlAKmF6aDYCywAWLX/YFZfYZ0OF9Kvz4o1\nXV8wbFg1u/+UN3DgwLqU01PqAT2zLvPnLazp+tUGDmTLLYfWXI96BMWBq9WnLiusUPuvyoGrDWTY\n0Lr8W2nK1zEr8xFW1cdy51fUc/38+nFQNDOz2okq9nDJNOHaMQ6KZmZWM/UR1eyeplDlDdIayEHR\nzMxqJqUjd/7Oq0pNHBSb3Lqr1P4+pF5aRrQ0ugpA89QDXJdyvjb8a42uwjLDv3Zgo6uwzIgm+fl0\nqmqiYhN2nYIXBO80koYBU764zqi6DJSp1aQZpVsSmpVX60CbeqnHQJt6qcdAm1pNnTaV7bbbFmCr\niJja6PoUFH7XbdPvaFbrs1bu6+YufYV/LboQmux5Gv+TNjOzbk99VNUfMmrSDlQHRTMzq13VLxWb\nMyh67VMzM6uZ+CAu5jryliuNlDRT0nxJ90raJkf+xyXNkzRd0jeqeQ63FM3MrGZSlVMycuSVNAI4\nEzgauB8YBUyWtFFEzC6T/zvAqcC3gAeA7YD/kfRWRPwtT726dUtRUh9Jv5D0bPZXwdOSflaS53ZJ\nS7NjvqTHsm9ccRk/yf6imCfpzeyvkSOK8oyTdHVJucOz8kZ1/pOamTU5deBo3yjggogYHxEzgGOA\necARFfJ/Pct/ZUQ8FxETgQuBH+d9jO7eUvwJ8G3gUOBxYGvgEkn/johzsjxB+qacCKwMHAacK+nN\niJgEnAQcBYwEpgCrZeWsXummkr4FnA18OyLGd8JzmZl1K1UPtGlnmTdJ/YCtgF8V0iIiJN0CbF/h\nshVYfhm8BcC2kvpGRLvLBXT3oLg9cE1E3Jh9/YKkg4FtS/LNi4g3gDeAkyUdBHwFmATsC5wXEcUt\nwUcq3VDSj4AxwIiIuLZOz2FmZq0NAvoCs0rSZwEbV7hmMvAtSddExFRJWwNHAv2y8krLWk53D4p3\nA0dJ2jAinpK0BbADqcndlgVA/+zz14BdJP2hXB91MUm/Br4D7BMRt9dWdTOznqXSa8JXFj7Eqwtb\ntzUWR6esa/4LYDBwj6Q+pN/vlwA/ApbmKaC7B8Vfk7o7Z0haQnpHekJETCiXOfsmHQx8Bjg/Sz4W\nuAJ4TdJjpEBb3Pos+BKpdbmrA6KZWYnC8NMy1lphS9ZaYctWaXMWv8w975zXVomzSaujlu69N5gU\n7JYTEQtILcVvZ/leJb1ieyfrLWxXdw+KI0hBroX0TnFL4HeSXomI/y3KN1LSUaTW4WLgrIg4HyAi\npgObSdqK1MrcCbhO0riIOLqojIdIze9TJO0dEe/lqeDDs6+hX58VW6Wtu8pQ1l21ObYbMrPmM2HC\nBCZObP23/Zy5cxpUm3zqPU0xIhZJmgLsClybrpGyr3/fzrVLgFeya1qA6/LWq7sHxTOA0yLiiuzr\nxyStB/wUKA6KfyIN050fEa+WKygippAG2vxe0iHAeEmnRsTzWZaXgeHA7cCNkvbKExg3H/SVpljm\nzcy6j5aWFlpaWq+VWrTMW1OqeqBNvrxnkQZPTuGDKRkDSF2iSDoNWCsiDsu+3pA0puQ+YA1ST+D/\nIw3GzKW7B8UBLL/5yFKWn2oyJyKeraLc6dnHlYsTI+JFSTsDt5HmyuyZt8VoZtazVdlUzDEnIyIm\nSRoEnELqDn0Q2LOoK3QIsG7RJX2B44CNgEWk39Wfi4gX8taquwfF64CfSXoJeAwYRvpL4o95C5B0\nBXAX6V3ia8AGpCHATwAzSvNHxEtZYLwduClrMb5T43OYmXVrnbXKW0ScB5R9+RgRh5d8PYMUBzqs\nW0/eB74HXAmcS3qneAbwB+DnRXna2wbkRuDLpD7rJ4BxWVl7RkTZ0UoR8QqwM7AmqSt1lRqewcys\n2yusaFPN0Yy6dUsx67o8Njsq5dmlnTIuAi5qJ8/hZdJeBTbJV1Mzsx4u/yo1H+RvQt06KJqZWZNQ\ndQNtmnWXDAdFMzOrnVuKZmZmSRpoU80uGZ1YmRo4KJqZWc06Y+uoRujuo0/NzMzqxi1FMzOrnaiu\nmdWcDUUHRTMzq11P6T51UDQzs5p11oo2Xc1B0czMatdDoqKDopmZ1a7+64E3hIOimZnVTFWuaON3\nir3U6VcdzLBhjd9QeNd+JzW6CgDcuuikRlehKS1ZUnbt+YZYcaV+ja5C02mGX+Bq1qZVgaiy+7TT\nalITB0UzM6tZD3ml6KBoZma185QMMzOzAk/eNzMzS3pKS9Frn5qZWe2yoJj3yPtSUdJISTMlzZd0\nr6Rt2sl/iKQHJb0n6RVJF0laI+9jOCiamVlTkjQCOBMYAwwFHgImSxpUIf8OwKXA/wCfBoYD2wIX\n5r2ng6KZmdVMAvWp4sjXUBwFXBAR4yNiBnAMMA84okL+zwIzI+LciHg+Iu4GLiAFxlwcFM3MrHaF\nLtFqjjaLUz9gK+DWQlpEBHALsH2Fy+4B1pW0d1bGYOBrwN/yPoaDopmZ1azOMRFgENAXmFWSPgsY\nUu6CrGX4dWCipIXAq8DbwPfyPodHn5qZWc3Up/Iyb8+99QDPv/1Aq7SFSxbUvw7Sp4HfAScBNwEf\nA8aSulC/lacMB0UzM6tdG8u8rbfmNqy3ZutBo2/Ne4Ebp5/eVomzgSXA4JL0wcBrFa75CXBXRJyV\nff2opO8Cd0g6ISJKW53LcfepmZnVrBATcx/tlBcRi4ApwK7L7pEmN+4K3F3hsgHA4pK0pUCQc7mA\nbh0UJY2TtLTomC3pBkmfKcpTfP7fku6U9MWi84Mk/UHS85IWSHo1K2P7ojwzJX2/5N5js/J26pqn\nNTNrYln3ad6DfDtqnAUcJelQSZsA55MC3yUAkk6TdGlR/uuA/5B0jKT1sykavwPui4hKrcvWj1HF\nIzerG0jN6SHALqS/Eq4ryXNYdv5zpCb5XyWtl527GtgC+AawIbAvcDuwZrmbSeoj6WLSy9wvRMQ/\n6/coZmbdVCeMtImIScBo4BRgGrA5sGdEvJFlGQKsW5T/UuBYYCTwCDARmA78R97H6AnvFN8v+ga9\nLunXwD8lrRkRb2bpcyLi9ez8McArwO6SJgE7AjtHxB1Z3heB1m+EM5L6AxOAYcCOEfF0Jz2TmVm3\n0lm7ZETEecB5Fc4dXibtXODc/DVprScExWUkrUJq8T1VFBBLvZ997Ae8mx37S7ovIha2UfyqpLku\nawOfi4hX6lRtM7Nuz5sMN499Jb2Tfb4yqRX45XIZJQ0AfknqYv1nRCyR9E3SEkDfkTQV+AcwISIe\nKbn8RGAusGkbAdfMrHcS1e180ZwxsUcExb+Tlv4RsDrwXeBGSdtExItZnj9LWgqsBLwOHBERjwJE\nxNWS/gp8nrRE0N7AjyQdGRHji+4zGdgNOIHUZ53LcaOPZeDAga3SWka00NJyUPVPama9woQJf2bC\nxAmt0ubMmdOg2uTTU3bJ6AlB8b2ImFn4QtJRwBzgKODnWfIPSUsFzSnXysu6TW/NjlMl/Q9wMlAc\nFG8FzgauldQnIn6Yp3Jnjj2LYcOGVf9UZtZrtbQctNwfzlOnTmXb7drcIMLqoCcExXICWLHo61kR\n8WwV108HvrJcoRG3SNqXFBgVET+osZ5mZj1CWhC8mpZiJ1amBj0hKK6QLfoKqfv0P0nzWEqnZSwn\n22PrCuBi4GHgHWAb4HjgL+WuiYhbJX0ZuC5rMf5n7Y9gZtbNVTn61O8UO89epME1kILaDGB40RSL\naOPad4F7Sd2rnySNSH2RtE7eaUX5WpUREbdJ2ocUGHFgNLNer7PmZHSxbh0Uszkqy81TKcnTt41z\nC0kDZ05op4wNyqT9A1gtX03NzHq2thYEr5S/GXXroGhmZs2hjfXAK+ZvRg6KZmZWO3efmpmZJZ6n\naGZmllGfdFSTvxk5KJqZWX00aeuvGg6KZmZWsx7yStFB0czM6qDKKRk5Nxnucg6KZmZWux7SVHRQ\nNDOzmvWUeYpNOv7HzMys6zkomplZzQrLvFVz5CpXGilppqT5ku6VVHH/LEnjJC2VtCT7WDhKN42v\nyN2nnWzp0qUsWbK00dXg1kUnNboKAOyx8imNrsIy189pc8nbLvWhD1VcorfLRbS1hr5ZBZ3wTlHS\nCOBM4GjgfmAUMFnSRhExu8wl3wd+XPT1h0g7IE3KWy23FM3MrGaFmFjNkcMo4IKIGB8RM4BjgHnA\nEeUyR8Q7EfF64QC2BT4MXJL3ORwUzcysZoVNhnMf7QRFSf2ArYBbC2mRujFuAbbPWa0jgFsi4sW8\nz+HuUzMzq12Va5/maCoOAvoCs0rSZwEbt1+8PgbsDbTkr5SDopmZ1YOoOM/iyRfu5skX7mmVtnDR\nvM6u0TeBt4FrqrnIQdHMzGomVR5RuvF6O7Dxeju0Snv9rZlMvLnNwW6zgSXA4JL0wcBrOap0ODA+\nIhbnyLuM3ymamVnNCltHVXO0JSIWAVOAXYvuoezru9upyxeATwIXVfscbimamVnt+qi69Uzz5T0L\nuETSFD6YkjGAbDSppNOAtSLisJLrjgTui4jp+SuUOCiamVnNOmPp04iYJGkQcAqp2/RBYM+IeCPL\nMgRYt3W5Wg04gDRnsWoOimZmVrO09mn+qJg3Z0ScB5xX4dzhZdLmAqvkrkgJB0UzM6td53SfdjkP\ntDEzM8u4pWhmZrWr8p1is+4d1fQtRUmDJZ0t6RlJCyQ9L+laSbtk558rWgn9XUlTJA0vun5MmVXT\nlxRdv5Kk0yQ9na3C/rqk2yTtW1TGbZLOKqnXD7L6HNhV3wszs2ZVmKeYf5m35oyKTd1SlPQJ0nyU\nt4DjgEeBfsBewDnAp4EAfgb8EVgNGA1MlLRDRNybFfUoaW5L8U/hrezjBcA2wEhgOrAm8LnsY6V6\nnQwcC+wbETfX/KBmZt1dZww/bYCmDorAH0grGmwTEQuK0qdLKp6U+W62IvrrkkYCXwf2BQpBcXHR\nEN5S+wLfj4jJ2dcvANMqVUjS2cDBwG4RcV/VT2Rm1gPlmZBfmr8ZNW33qaTVgT2Bc0oCIrBs2O1y\nImIJsAjon/NWrwFfktTeEN5+kv4EfBXYyQHRzOwD6lP90YyauaX4KVJ35xN5L5DUn9TNuhpF240A\nm0uaywfdp49FxGezz48G/gS8Kekh4E7gyogoXUboKFJX7RYR8WS1D2Nm1pOl3tNqWoqdWJkaNHNQ\nrOZbdrqkU4EVgXeAH0fEjUXnZ5C6SQtlvl84ERF3SNoA+CzpXeKuwB2Sfh4RpxaVcQewJfBLSQdl\nLdJ2jT5+NAMHDmyVNuLAEYwYUdVuJmbWi0yY8GcmTJzQKm3OnDkNqk1ePWP4aTMHxadILbNNaH/r\nj9+Q1sIrvFsstTAiZla6OAtwd2XHbySdAJwo6fSiFdYfIbVCbyUN5DkwIpa29xBjfzOWoUOHtZfN\nzGyZlpaDaGk5qFXa1KlT2Xa7bRpUoxyq7RJt0u7TJq0WRMTbwGRgpKSVSs9LKm5+zY6IZysExI6Y\nTvqDYcWSOj1MaknuBFwhqW+d7mdm1q3Ve5eMRmnaoJgZSdp5+X5JX5X0KUmbSPo+7Wwdklc2B/Fo\nScMkfULSl4BTgb9HxLul+bPAuAuwIykwNnNr28ysa0gfLPWW53BQrF7W5TkMuA0YS+rCvAnYgzRP\nEFIXay1uBA4ltUofB34H3ACMKK5KSb0eJQXG7YFJDoxm1tv1lJZi0/8yj4hZpC1Aym4DEhEbtHP9\nycDJbZw/HTi9nTJ2KZP2GPCxtq4zM+stesjc/eZuKZqZmXWlpm8pmplZN9CHKreO6rSa1MRB0czM\naiaqXObN8xTNzKzH6hlz95u1AWtmZt1KNdMxCkcOkkZKmplt7XevpDZXMJDUX9Kp2baCCyQ9K+mb\neR/DLUUzM6tZZ+ySIWkEcCZpjer7gVHAZEkbRcTsCpddAXwEOBx4hjRLIHcD0EHRzMxqVthkuJr8\nOYwCLoiI8dk1xwD7AEcAZ5Qpcy/g88AGEfHvLPmF3JXC3admZlYP6sDRVnFSP2ArinY8iogAbiEt\nnFLOvsADwI8lvSTpCUm/kbRihfzLcUvRzMxq1glbRw0iLfM5qyR9FrBxhWs2ILUUFwD7Z2X8AVgD\nODJPvRwUzcysZm11nz7y2G08+tjtrdIWLFhuael66AMsBQ4urF0t6VjSOtXfjYj327waB0XrYte+\n+dNGV2GZ4Ruc1egqLPOXF45vdBWWWbq01uWEe54+TfCiKWpe5rmTtTHQZvPNdmHzzVqvlvnKq09x\n4UUj2ypxNrAEGFySPhh4rcI1rwIvl2zmMJ3UWbsOaeBNm5rgR21mZt1end8pRsQiYAppu750ixR1\nd6XyLkl3AWtJGlCUtjGp9fhSnsdwUDQzs5p10i4ZZwFHSTpU0ibA+cAA0qbySDpN0qVF+S8H3gTG\nSdpU0k6kUaoX5ek6hQ50n0oaAhARr2Vfbwm0AI8Xhs2amZnVKiImSRoEnELqNn0Q2DMi3siyDAHW\nLcr/nqTdgbOBf5EC5ETgxLz37Mg7xYnAOOASSR8l7XU4Ezha0toRcVoHyjQzs26ss7aOiojzgPMq\nnDu8TNqTwJ75a9JaR7pPPwPcm31+IPBERAwDDiHnkFczM+tZxAeBMdfR6ApX0JGW4grA/Ozz3YBr\nss8fBdauR6XMzKx76Yxl3hqhIy3Fx4EjskVZdwduzNLXAt6qV8XMzKwbqaaVmGP0aaN0JCj+F3As\nqQv1moiYlqV/mfRi08zMepnCijb5j0bXuLyqu08j4uZsNNAaEfFq0an/BTpliQIzM2tunTXQpqt1\nZEpGP2BpISBKWgvYD5geEf+oc/3MzKwbEFW+U2zS/tOODLS5LjvOlbQaaUXyvsCHs7XlLqpnBc3M\nrPn1lJZiR94pbgUUWoTDSZMj1wa+SXrXaGZmvY6q+q9ZR9p0JCiuAszJPt8DuDoiFpPWnFuvTvXK\nRdJgSb+T9JSk+ZJelXSHpGMK+2dJek7S0pLjhaIyNpd0jaRZWRkzJf05e2+KpE9k12xedM0qkm6T\n9GjWfWxm1qtVNUexylZlV+pI9+kzwJck/R9p1YCzs/RBdOFAG0nrkxaFfQv4CWme5PukxQWOJi3+\n+lcggJ8Bfyy6fElWxiDSBpbXkgL8v0mBfT9gZdIq7WRlFO77EeAGYBGwY9HuzmZmvVZP6T7tSFA8\nFRgPnAvcGRF3Zem7kdal6yp/ABYCW0XEgqL050jvPIu9GxGvlyljB2A14KiIWJqlPc8H3cMFApC0\nLnAT8CKwf0TMq+kJzMx6iF47eT8i/gx8krS78W5Fp+4GjqtTvdokaQ3SwgHnlATEar1G+sPgq+3k\nC2AT4E5Si3QfB0Qzs56nQ1tHRcQLEXFP9i6xkHZnRDxav6q16VOk1tuTxYmS3pD0TnYUL0x+elH6\nXEnfy+p8H/Ar4DJJsyVdL2l0ttB5q6JJreOngAOzfb7MzCzTm9c+JRt0Mhz4ONC/+FxEHFyHenXU\nNqRAfzlpjdaC35Dtv5UpvCskIk6UdBawC7AdcAzwX5I+HxGPFV1zDbA/8B/AlXkrNPr40QwcOLBV\n2ogDRzBiREveIsysl5kwYQITJ05olTZn7pwKuZtED3mp2JHJ+18FJpDeu+2UfdwQWB24vq61q+xp\nUpfmxsWJEfFcVsf5JflnR8SzlQqLiLeBq4CrJP0X6d3oaKCwLUmQ3qU+AlwuSRFxRZ6Kjv3NWIYO\nHZYnq5kZAC0tLbS0tP7Deeq0qWy33bYNqlEO1Y4obc6Y2KHu058DP4qI3UkDXY4hBcW/AI+1dWG9\nRMRbwM3A9yStVOeyF5NG2K5clKzs3C+Bk4A/STqwnvc1M+vOeu3ap6QAWNguaiGwckQslnQGKVCd\nWq/KteO7pIEvD0g6GXgYWApsSxoU0+7i5JL2AVpILd8nScFvP2Bv0mIEy4mIX0laQnoP2SciJpTL\nZ2bWm/SQ3tMOBcW3+aAV9QqwKalbcRVg1TrVq10R8aykoaRdO34FrEOap/g46R1iYafmKF8CZHnf\nA8YC62bXPwUcGRGXF9+u5N6nS1oKjJeEA6OZ9Xa9ee3Tu0iDUh4F/g/4naTPA3sBt9evau2LiFnA\nD7KjUp72NEb9AAAgAElEQVQN2jg3k9T929Y9niet7Vqa/htS8DUz6/U6q6UoaSRpjMcQ4CHgPyOi\nbE+gpJ2B20qSA/hYhbnqy+lIUPxPoPAe7xRSl+XnSJPax3SgPDMz6+aqXc00T15JI4AzSauU3Q+M\nAiZL2igiZle4LICNgHeWJeQMiNCx/RRfL/p8MWngiZmZ9WZVrmiTs6k4CrggIsanS3QMsA9wBHBG\nG9e9ERFz81fmA7lGn0rqn/foSCXMzKx7q/eC4NnevVuR1qcGICICuAXYvq1LgQclvSLpJkmfq+Y5\n8rYUF9D2gJViy71/MzOznq0T1j4dRIons0rSZ1EyR73Iq8C3Sfv8rgAcBdwuaduIyLU2d96guHfO\nfGZm1gu11fr71wM3868Hbm6VNn/Be3WvQ0Q8SevlP++V9ElSN+xhecrIFRQjYnL11TMzM4Nttt6d\nbbbevVXaCy8+wa9OP6Kty2aTtvkbXJI+mLSZQ173k3ZEyqXqFW0kHSLpgDLpX5V0ULXlmZlZ91eY\np5j7aGf8abbxwhRg12X3SH2uu5J2ZcprS1K3ai4dWebtRNIE/lJvk5aAMzOz3qbaQTb5Xj+eBRwl\n6VBJmwDnAwPINniQdJqkS5dVQfqBpP0kfVLS/5P0W+CLwDl5H6Mj8xTXA2aWSZ8JfKID5ZmZWTfX\nGZP3I2KSpEGkOfGDSZs17BkRb2RZhpBWIyvoT5rXuBYwj7T8564R8c+89epIUHwD2Iy0Q32xzYB/\nd6A8MzPr5jph9CkAEXEeHyzbWXru8JKva15prCPdp5OA30taNk8kmwfyu+ycmZn1MvWep9goHWkp\nnkDa+f6uon0LVwQmAj+tV8V6ij59+tC3b0f+9qivJUuWNroKAKy4Yr9GV2GZv7xwfKOrsMweK5/S\n6Cosc8PcnzW6CtYNifytv0L+ZtSRZd4WAF+R9BnSqJ75wMPZ/BAzM+uNOmPx0wboSEsRgIh4hLRl\nlJmZ9Xads/Zpl+twUDQzMyvorIE2Xc1B0czMatZZ+yl2NQdFMzOrWWFFm2ryNyMHRTMzq1lPaSl2\naK6ApG0l/VHSbZLWytJaJH22vtUzMzPrOh1ZEHw/4B+kvaq2J81RBPgo4AlOZma9UTWLgTfx7P2O\ntBTHAN+LiG8Ai4rS7yTtkmxmZr1MinPVBMZG17i8jrxT3AS4tUz6v4HVa6uOmZl1R735neLrwPpl\n0ren/O4ZZmbWw9V7P8VG6UhLcRzwW0mHAgGsKWkoMBY4o56VMzOz7kF9hPpUMSWjirxdqSMtxV8C\n1wL3AKsA9wKXA3+KiP+uY93KkjRO0lJJSyS9L+kpSSdK6lOSb4ak+ZI+WqaM27MyflTm3N+yc2U3\nTJZ0fnb++/V7KjOzbq5zNhnuclUHxYhYGhEnAh8BtibtajwkIrpyy4EbSJtLfoq0d9YYYHThpKQd\nSKNjrwS+Web6AF4oPZdNL9kFeKXcTSUdAGwHvFxj/c3MepTqBtlUuU5qF+rwnkYR8V5ETI2If0bE\n2/WsVA7vR8QbEfFiRFwI3AJ8pej8kWStV+CICmX8FRhUvC8kcBgwmfTetBVJa5P2jDwYWFz7I5iZ\n9Rxp66gqjkZXuIKq3ylKur6t8xHxpY5Xp8MWAGsCSFoV+BqwDfAkMFDSDhFxV8k1C4HLSEHznizt\nm8DxwMnFGZX+pBkPnBER05v1Lxwzs0bpKQuCd6Sl+HzJ8Qpp4v7nsq+7lKTdgD35YJpIC/BkRMyI\niKXAn0ktx3LGAQdKWknSTsBqpBZkqZ8ACyPinPrW3sysZ+iseYqSRkqamY0RuVfSNjmv20HSIklT\nq3mOjmwy/J0KFfgVXdci3lfSO0C/7J6X8UHr7nBSt2nB5cDtkv4zIt4rLiQiHpb0JKll+UVgfEQs\nLf4LRtJWwPeBoR2p6HGjj2XgwIGt0lpGtNDSclBHijOzXmDChAlMnDihVdqcuXMaVJucql2kJkde\nSSOAM4GjgfuBUcBkSRtFxOw2rhsIXEp6tTa4ilrVdUHwcaRuyJ/WscxK/g4cQ1pR55WsRYikTYHP\nAttIKp4e0ofUgryoTFnjgJHApqQu11I7kgYVvVgULPsCZ0n6YURs0FZFzxx7FsOGDcv7XGZmtLS0\n0NLS0ipt6rSpbLfdtg2qUQ6dM3t/FHBBRIxPl+gYYB/Sa6+2pgCeT2osLaX1eJN2dXigTRnDaL3s\nW2d6LyJmRsRLhYCYOZK0LuvmwBZFx39TuQv1cuAzwCMR8USZ8+PLlPcK6QeyZx2exczMSkjqR1o6\ndNkKahERpNbf9m1cdzhpgZmTK+VpS0cG2lxemgR8DNiBBk7el/Qh4BvAzyJiesm5PwLHStq09FxE\n/FvSECoE9GxkbavRtZIWAa9FxFP1fAYzs+6qE/ZTHETqlZtVkj4L2LhsmdKGwK+AHUtfheXVke7T\n0rssBR4EzoqIaztQXr3sB6wB/KX0RETMkPQ4qbU4usz5uaVJ7dyrvfNmZr1KW72n/7zjeu64o/XE\nhffee6fO91cfUpfpmIh4ppBcbTlVBUVJfUldkU9EREPe+kbE4RXSryYNvKl03WZFn3+xnXu0+RKw\nvfeIZma9TVvLvO288z7svPM+rdKeeeZxjj32a20VORtYwvIDZQYDr5XJvyppQZktJZ2bpfUhzapb\nCOwREbe38xjVvVOMiCXAHWRzAs3MzKDKifs5xuRExCJgCrDrB/eQsq/vLnPJXGAzYEs+GP9xPjAj\n+/y+PM/Rke7Tx4F1gWc7cK2ZmfVI1S7dlivvWcAlkqbwwZSMAcAlAJJOA9aKiMOyQTiPt7qD9Dqw\noHQsSVs6EhR/BIyV9FNSFC+d+7ewA2WamVk3Vpi8X03+9kTEJEmDgFNI3aYPAntGxBtZliGkRlrd\ndCQoTi75WKpvB+tiZmbdVGdtMhwR5wHnVThXdoxJ0fmTqXJqRkeC4t4duMbMzHqwnrL2ae6gmO0v\nODYiKrUQzcysl+opQbGa0adjSJsKm5mZLadeI08bqZru0yZ+DDMza6Se0lKs9p2iV3IxM7Pl9Nag\n+KSkNgNjRKxRQ33MzMwaptqgOAZo8k29zMysq3XWlIyuVm1QnBARr3dKTXqoIEgLLTRW37713CXM\n6u2m937e6Cosc8F55VbQ6noDBlRcyrjLff2wrRtdhaYnUXHt00r5m1E1QbHxv9nNzKw5VTuqtAcE\nxSZ9BDMzazRl/1WTvxnlDooR4f43MzMrT1TXdGrOmNihZd7MzMxa6a1TMszMzJbTW0efmpmZLUdV\n7qfY7d8pmpmZVdQLR5+amZmV5XeKZmZmGb9TNDMzy6Sg2P1XtPHcQzMzs4yDopmZ1ayaDYar6WqV\nNFLSTEnzJd0raZs28u4g6U5JsyXNkzRd0g+reY6mCYqS1pF0saSXJb0v6TlJv5W03FZUkg6StFjS\n2WXO7SxpqaQ3JfUvObd1dm5JUdoKksZJeljSIklXV6hff0mnZvVaIOlZSd+sw6ObmXV7osqgmKdM\naQRwJmmHpqHAQ8BkSYMqXPIecDbweWAT4BfALyV9K+9zNEVQlLQ+8ADwSWBE9vHbwK7APZI+XHLJ\nEcDpwEGlga/IO8ABJWlHAs+XpPUF5gG/A25uo5pXAF8EDgc2Ag4Cnmgjv5lZL6Kq/ss5J2MUcEFE\njI+IGcAxpN/XR5TLHBEPRsTEiJgeES9ExOXAZFKQzKUpgiJwHvA+sHtE3BkRL0XEZGA3YG3g1ELG\nLIBuD/waeAr4aoUyLyUFwcJ1KwItWfoyETEvIkZGxEXArHIFSdqL9E39UkTcln2z74uIezr2uGZm\nPUu9u08l9QO2Am4tpEXah+8WUgzIUScNzfLenvc5Gh4UJa0O7AGcGxELi89FxCzgMlLrseCbwN8i\n4h3gT0C5ZnEA/wt8XtI6WdpwYCYwrQPV3JfUkv2xpJckPSHpN1mgNTPr9QrzFKs52jGI1JNX2liZ\nBQxppy4vSloA3E+KLePyPkczTMnYkNSOnlHh/HRg9awP+U1SUByZnZsAjJX0iYgo7RZ9Hbghy/9L\nUrfnxR2s4wakluICYH/SD+sPwBoUtUbNzHqrtlp/kyf/hcmTr2mV9u6773RmdXYEVgE+C5wu6emI\nmJjnwmYIigXt/dmwkNSiHEAKdkTEm5JuIfUvjylzzcXAbyVdRvrmDAd26kDd+gBLgYMj4l0ASccC\nV0j6bkS8X+nC0aOPY+BqA1uljRjRQktLSweqYWa9wYQJE5g4cUKrtDlz5zSoNvm01frba68D2Guv\n1kM8Zsx4hK9/fe+2ipwNLAEGl6QPBl5r68KiRtJjkoYAJwHdJig+Teru3BS4psz5TwNvRMRcSUeS\nWmcLir75Aj5D+aB4A3AhcBFwXUS83cGlhV4FXi4ExMz07N7rAM9UunDs2DMZNnRYR+5pZr1US8vy\nfzhPnTaV7bbbtkE1yqeeE/IjYpGkKaQBl9em8qXs699XUVRfYIW8mRv+TjEi3iKN+vyupFYVzyL8\nwcC4bGrGfqT3i1sUHUNJ3at7lCl7CTAe2JkUGDvqLmAtSQOK0jYmtR5fqqFcM7MeoRPeKQKcBRwl\n6VBJmwDnk3oLL8nueZqkZYMnJX1X0pclfSo7jgSOI40xyaUZWooA3yMFnsmSTiQNiNkMOIP0rvEX\nwNHA7Ii4svRiSTeQBtzcVEgqOv0z4Iws+JYlaVPSXxJrAKtI2gIgIh7KslyelTNO0knAR7K6XdRW\n16mZWa+Re5ZFUf52RMSkbDzJKaRu0weBPSPijSzLEGDdokv6AKcB6wGLSb14x0fEhXmr1RRBMSKe\nzlYpOInU7/tR0sNdBXwjIhZIOhwoO7E+yze+aKJ/FJW9GKgYEDPXAx8v+npaVkbfrIz3JO1OmhT6\nL9KAn4nAiXmf0cysJ+ustU8j4jzStL1y5w4v+foc4JzclSijKYIiQES8QNGETEljgGOBzYH7I2KL\nNq69gjS5HuAfZMGsQt5rSs9HxPo56vcksGd7+czMeiPvktHJIuJkSc+RRo3e3+DqmJlZL9C0QREg\nIi5tP5eZmTWaqHKT4apeQHadpg6KZmbWfTRnmKuOg6KZmdWsimkWy/I3IwdFMzOrmQfamJmZZdxS\nNDMzy7ilaGZmVqRZA101HBTNzKxm7j41MzPLuPvUzMws01lrn3Y1B0Uza2Xgh1dqdBUAmP3Ge42u\ngvVCDopmZlaznvJOseGbDJuZmTULtxTNzKwumrTxVxW3FM3MzDJuKZqZWc16ypQMtxTNzKxm6sB/\nucqVRkqaKWm+pHslbdNG3gMk3STpdUlzJN0taY9qnsNB0czMaqcOHO0VKY0AzgTGAEOBh4DJkgZV\nuGQn4CZgb2AYcBtwnaQt8j6Gg6KZmdWs0H1azZHDKOCCiBgfETOAY4B5wBHlMkfEqIgYGxFTIuKZ\niDgBeArYN+9zOCiamVnN6t19KqkfsBVwayEtIgK4Bdg+V53SZMhVgbfyPoeDopmZ1a7+3aeDgL7A\nrJL0WcCQnLU6HlgZmJQzv0efmplZfVSKc9dccxXXXndVq7S5c+d2bl2kg4ETgf0iYnbe6xwUzcys\nZqLyMm/77z+c/fcf3irtkUceYp8vf6GtImcDS4DBJemDgdfarIvUAlwIDI+I29qseImm6T6VtI6k\niyW9LOl9Sc9J+q2kNcrkPUjSYklnlzm3s6Slkt6U1L/k3NbZuSVlrhst6QlJCyS9KOmnFeq5g6RF\nkqbW8rxmZj1KnbtPI2IRMAXYddktUtTdFbi7YjWkg4CLgJaIuLHax2iKoChpfeAB4JPAiOzjt0kP\nf4+kD5dccgRwOnBQaeAr8g5wQEnakcDzZe7/+6zMY4GNgf2A+8vkGwhcSnrRa2ZmmU6YkQFwFnCU\npEMlbQKcDwwALgGQdJqkS5fVIXWZXgocB/xL0uDsWC3vczRFUATOA94Hdo+IOyPipYiYDOwGrA2c\nWsiYBdDtgV+Thtp+tUKZl5KCYOG6FYGWLJ2i9E1Jw3z3i4i/RcTzETEtIm5leecDlwH3duwxzcws\nr4iYBIwGTgGmAZsDe0bEG1mWIcC6RZccRRqccy7wStHx27z3bHhQlLQ6sAdwbkQsLD4XEbNIQWhE\nUfI3gb9FxDvAn4BvlSk2gP8FPi9pnSxtODCT9I0t9mXgGWA/Sc9mKyf8T1av4noeDqwPnFz9U5qZ\n9WyFTYbzH/nKjYjzImK9iFgpIraPiAeKzh0eEbsUff3FiOhb5ig7r7GcZhhosyGpJT2jwvnpwOrZ\nCgZvkoLiyOzcBGCspE9ERGm36OvADVn+XwKHAxeXKX8DYD1S0Pw66XvyW+AKUksVSRsCvwJ2jIil\n1ewDNnr0cQxcbWCrtBEjWmhpacldhpn1LhMmTGDixAmt0ubMndOg2vQuzRAUC9qLNAtJLcoBpGBH\nRLwp6RbS+8AxZa65GPitpMuAz5IC304lefoA/YFvRMQzAJKOBKZkwfAZUmt1TOF8jrouM3bsmQwb\nOixvdjMzWlqW/8N56rSpbLfdtg2qUfu8IHj9PE3q7ty0wvlPA29ExFzSO8I1gAXZCNBFpDXuDqtw\n7Q2kIHoRcF1EvF0mz6vA4qKAB6l1CvBx0moIWwPnFN3zRGBLSQslfSHnc5qZ9VxVdZ1WGUG7UMOD\nYkS8BdwMfFfSCsXnJA0BDgbGZVMz9iO9X9yi6BhK6l5dbiX0iFgCjAd2JgXGcu4CPpQN4CnYmBSo\nnwfmApsBWxbd83xSd+8WwH3VP7WZmTWjZuk+/R4pOE2WdCJpQMxmwBmk4PML4GhgdkRcWXqxpBtI\nA25uKiQVnf4ZcEYWfMu5BZgKXCxpFGnk0jnATRHxdJbn8ZL7vQ4siIjpmJlZmmZRTfdpp9WkNg1v\nKQJkwWcb4FlgIvAccD3wBGlwyzzSQJmrKxRxFbBv0UT/KCp7cRsBsbDA7L6k1RP+AVwHPAYcVMMj\nmZn1Kp21n2JXa5aWIhHxAkXbgUgaQ5pMvzlwf0RU3A8rIq4gjRaFFNj6tpH3mtLzEfEa8LUq6noy\nnpphZvaBKmbkL8vfhJomKJaKiJMlPUcaNbrc6jJmZtY8esro06YNigARcWn7uczMrNF6SEOxuYOi\nmZl1Ez2kqeigaGZmddGcYa46TTH61MzMrBm4pWhmZjXrIb2nDopmZlYP1S7d1pxR0UHRzMxq5tGn\nZmZmGXefmpmZtdKkka4KDoqdbPGipSxatKTR1aBfv4or33WpajZo7k3SErzN4avDP9PoKgDQt2/z\nDI4/4BNjG10F5ix6udFVaFNPaSk2z786MzOzEpJGSpopab6keyVt00beIZIuk/SEpCWSzqr2fg6K\nZmZWO33QWsxz5OlplTQCOBMYQ9o79yHSFoODKlyyAvA6abvBBzvyGA6KZmZWB+rA0a5RwAURMT4i\nZgDHAPMo2lGpWEQ8HxGjIuJPpA3iq+agaGZmNaumlZjn/aOkfsBWwK2FtGz/21uA7TvrORwUzcys\nGQ0i7X07qyR9FjCks27q0admZlYfFVp/V145iSuvnNQqbc7cOV1Qoeo5KJqZWacaPvxAhg8/sFXa\ngw9OY+cv7NDWZbOBJcDgkvTBwGt1rWARd5+amVnN1IH/2hIRi4ApwK7L7pEmOu8K3N1Zz+GWopmZ\nNauzgEskTQHuJ41GHQBcAiDpNGCtiDiscIGkLUgduasAH8m+XhgR0/Pc0EHRzMxq1hkr2kTEpGxO\n4imkbtMHgT0j4o0syxBg3ZLLpgGFJaKGAQcDzwMb5KmXg6KZmTWtiDgPOK/CucPLpNX0WtBB0czM\natdDFj/t1gNtJK0j6WJJL0t6X9Jzkn4raY2iPLdLWpod8yU9Juk7Ref7SPqJpOmS5kl6M1tf74ii\nPOMkXV1y7+FZeaO65mnNzJpXp6xn0wDdNihKWh94APgkMCL7+G3SyKR7JH04yxrAhaT+6E2BScC5\nkgrjg08CfgCckJ3/AnABULi+3L2/Bfwv8O2I+O96PpeZWbfUQ6Jid+4+PQ94H9g9IhZmaS9JehB4\nBjgVGJmlz8tezL4BnCzpIOArpAC5L3BeRBS3BB+pdFNJPyItTjsiIq6t5wOZmXVnTRrnqtItW4qS\nVgf2AM4tCogARMQs4DJS67GSBUD/7PPXgF3aWHW9+L6/JrUo93FANDMrUu/FTxukWwZFYEPSHyUz\nKpyfDqxeGuiy94dfBz7DB4vMHgt8BHhN0kOS/iBprzJlfgk4HvhKRNxeh2cwM7Mm0527T6H91nqh\nFTlS0lGk1uFi4KyIOB8gm9C5maStgB2AnYDrJI2LiKOLynqItEDtKZL2joj38lTwRz8ezcCBA1ul\nHfi1ERx4YFsNWTPrzV6e/yCvzH+oVdqiWNCg2uRT7WvC5mwndt+g+DRpAM2mwDVlzn8aeCMi5qZV\ngfgT6R3j/Ih4tVyBETGFtKTQ7yUdAoyXdGpEPJ9leRkYDtwO3ChprzyB8YzTxzJ06NCqHs7Mere1\nV9qStVfaslXanEUvc8fssxtUo5yaNdJVoVt2n0bEW8DNwHclrVB8TtIQ0goG44qS50TEs5UCYhmF\n5YBWLrnvi8DOpFUUJktaufRCM7PeqN5rnzZKtwyKme8BK5CC0+ezOYt7ATeR3jX+Ik8hkq6Q9ENJ\n20r6uKQvAOcAT1DmnWVEvEQKjB8FbpK0an0ex8zMGq3bBsWIeBrYBngWmAg8B1xPCmY7RsS8QtZ2\niroR+DJwbXbtOOBx0vp6Syvc+xVSYFyT1JW6Sk0PY2bW3XmeYuNFxAtA8cozY0ijSTcnrahOROzS\nThkXARe1k6fc+nqvAptUX2szs57HA22aUEScLOk54LNkQdHMzLpAD4mKPSooAkTEpY2ug5lZ79Sk\nka4KPS4omplZ1+shDUUHRTMzq4MeEhUdFM3MrGY9JCZ23ykZvcWkSRMbXYVlJkyY0OgqADBhwp8b\nXYVlmqsuzfHzaaZ/sxMnNsf3BNLSbT2aFwS3rjDpCv+CKTWhSeoBzVWXZvn5NNW/2SYK0KVrmVo+\nkkZKmplt6n6vpG3ayf8FSVMkLZD0pKTDqrmfg6KZmdWu2kZijoaipBHAmaQ9bIeSNmaYXGmrP0nr\nAX8l7YK0BfA74I+Sds/7GA6KZmbWrEYBF0TE+IiYARwDzKNo0ZYS3wGejYgfRcQTEXEucGVWTi4O\nimZmVjMhpCqOdpqKkvoBW/HB3rdERAC3ANtXuOyz2flik9vIvxyPPu08KwI88USlfZDzmTNnDtOm\nTau5Mh/qV/vfP3PmzmHqtKk1lVGPlfHnzJnD1Km11aNe6lWXaHeJ3hx1qcPPB2DxorJL/uavR53+\nzfbtW59/K9Pq8D2Zs+jlmstYFAtqKufdxa8XPl2x5sp0ghkzprefqbr8g4C+wKyS9FnAxhWuGVIh\n/2qSVoiI99u7qVLgtXqTdDBwWaPrYWY9ziERcXmjK1Eg6eOk7fYGdODy94GNsnWsS8v9GGkf2+0j\n4r6i9NOBnSJiudafpCeAiyPi9KK0vUnvGQfkCYpuKXaeycAhpN07mnvLbDPrDlYE1iP9bmkaEfGC\npE1JLbtqzS4XEAvngCXA4JL0wcBrFa55rUL+uXkCIjgodpqIeBNomr/mzKxHuLvRFSgnC2yVgltH\ny1wkaQqwK2lrPyQp+/r3FS67B9i7JG2PLD0XD7QxM7NmdRZwlKRDJW0CnE/qpr0EQNJpkoo3gTgf\n2EDS6ZI2lvRdYHhWTi5uKZqZWVOKiEnZnMRTSN2gD5I2gH8jyzIEWLco/3OS9gH+G/g+8BJwZESU\njkityANtzMzMMu4+NTMzyzgompmZZRwUuylJH5e0eaPr0cwkNfTft6RPSdqikXUws+o4KHZDklYH\npgIbNrouAJI+Imm1RtejmKRPA4dJ6tug+69Omk/26Ubcv5xsOHvTkbRKo+tQrBH/ZiTtL2mdrr6v\nLc9BsXvqQ1oUt7Y15Oog+x/5IWCjRtelIPuldi6wcUQsaVA13gH6Aa82OhhJWkVSv4iIRtellKQf\nAN+RNKQJ6nKApNUjYklXBkZJXwYuBd7rqntaZQ6K3dPKpI1X5jW6IsDHgUXAI42uSEEWCFcmrYjR\n5bJu2zVJQfHNaOAQb0kbATcAB2drPzZNYJR0BmlLoKdIy301si5HA1cBl0tas4sDYz/ScmbvdtH9\nrA0Oit2EpCGS1sq+XJ20pFK/BlapYDVgKbC40RUpyILSItIvmq6871qSBkbEUtLPZkXS96YhsuB3\nNLADcDDwFUn9myEwStofOBDYKyL+AvSRNEjS+g2q0hJgWvbxskJg7KJ79wfejohFXXQ/a4ODYjcg\naSDwR+B8SR8F3gDmk7UUJX2oMKikKwaXSFpNUmHx3/6kX/4rNXJgi6R1JX1d0oeyoLQGKTB2ybs0\nSf2BvwPXSFqV9LNZQAODYtZCvReYmNXlZ8B/FJ1rpPWBqRFxv6T9SK20e4F/SPp5A+rzCvA2MAn4\nMPAnSP92JK1d75tJ2q3oXeq6wCqNHhhmiX8I3UBEzAFuI7XKxgI7AY8B/bM9x1Yl/U/1IVJw2rSz\n6pK9+5kMfDMLNktIAWAeEKUBuosCUh/gZPj/7Z15vJVV1ce/P1ARMEdyCnFATWUQ0SQVU8rSNIe0\nTMVIEw3nV3NK83XWRLMccqCQXs0y05yKyixwigRFQ1F5RYVUfLumOOFAyHr/WOt4Hw73AnLPc+7l\n3vX7fPbnnGfv/Zy9zt7Ps9dea6+1NqcCQ6PNj872rgcDMLO5wDeBjfHTUTbF+6SHpFUlrSVp/ZD4\n15C0raQ1yqYLZ8rdgH3w2JQnS9pN0vWS9q1D+wugMPH3Bl6PBd91wG+BM4CLgNPjJIR60STgdeAD\nM7sB+DHQRdJ4fJ9vh1qqUiUNBq4CfhBZc4C5FM6iL7bX2lJ9R0NGtGnDkLQqsJqZvRDXI/CV/ppA\nP3ySWwVnTBV0Bt4APmtm1eeK1Yquu/BJbSSuxt3DzD7fTN0eZlb63l6oln8IrI+v9o/AJ7cZ+GQz\nD85xBLQAABnJSURBVA9r2AmXIqcXj6NpQbtrAJ3NrCGuBwB/xg1t1qHxPLgVgZXwfaP5QVO/Eseo\nk5nND6Zzq5l9MfJvB3bE1bu7mNkkSaq35ChpPzwU1+34eBxqZvOi7OvA9UFfi8doCempSPoHxakP\nx+NM601gazN7WVLnWqhUJXUFTgN2Be7Hn821gEuAt/DntCuuDfoQNxj7a0vbTSwZMvZpG4U8+O1I\n4HlJ15rZM2Z2bSwaD8MtT68BnsQnOOEv0hzg+VpPtvEidzGzN8xsL0m/AI7FJ/ydJU3CjVvewZ+r\nLkHTLEn7mtlbtaQnaFobd3l4ysxmxUR2DTAM2Ay4Gl84rIb30Qf4irwTMLgG7W8e7U2TdI6ZzTKz\nxyXtAtyM98Vx+HE2FjTMxSe6F0sYo57Ahmb2QDDETtHu+pK2NrNHg6ZuwEygp6QpS3qkTg3oKzKV\nycAkfJE3qcIQA0/gklspe+bBdGdXxcNcHmfOPSS9g6uaJ+HP8ChJhxTibS5tu/3wI4xmSroQXxzt\nCGyLL5h2wWN5/ifKOuHv0Q04w07UAckU2yDi5bkXuAO43cw+cr0IxtgZj/w+ELjDzF6M+0pZ8YcF\n42nABEm/M7NXzOxgSWNwBjQBGI+vcitMpys+Af+5JIbYB58s/oFbDDaYWUNI05dHtT/iq+85uHQ9\nB99b62pmr7ew/X74f74B/4+zKmVm9g9J38Alxj2BI8ysVMtC+UGvjwGPSRppZvfE3upbkv4GvCvp\nGmAIsBNwOi6pGf6clUnb9sCEikWnmX1oZi9IuhnYCthD0u5mNjZumYPv75XxLF+Cvzunyo2i3gQw\nszkhRe8BHIUfSnsMPn5nA9/Cty6Wtt3TcKY3XtJVZvZGqIg/xLUtbwIn4v+9e1x3A1Yws0eWtt3E\nUsDMMrWhBPQEnsdVN52qylT4fiTwIO7ftEGJ9PTHrThvBPaKvE6F8jH4/uZQYMU69VFfXJL4IdC/\nun9wVdRtwAPAtwvlnWrU/rrAU8D5TZQVx2ggbhR1B9Cj5D7ZG5cu/hc/x3PXQtk1UfYK8JnIWw6X\nZjcqma7/xlXJ5xfGZ/lC+X7Aw9FPFwIn4NqP35RAy9G4ZmO7ZsrPin4ahZ/SDr7A+2wL2x0Z79DB\nlf6uPIu4Wv0cfGE5Elipmd+oybObaQnGq7UJyFQ1IHAAcB/wiULepyN/FPDdQv4IXNV0HbBcCbRs\niB+9clH171cxxptxde4hwMol98/qsRi4uImyLvgeLPh+3q24gdIxNaZhF/yw13Xx/cQKoz4omPEp\nwMDIH4DvGd1U9sQGjMal00nA3cCXIn97/Py5Ck2d6/QsDwOeA8bGmJ3XDGPcFjgTmB50X1koUw3o\nEL63+wvgpMj7QvTJn/CF3VqRv0eFMVW3vTS0AIcCLxKLkaqyrvG5Ir54+BsuvXevx/hkambMWpuA\nTFUDAocHg9k4rofFpPJcvDTzgF8U6h9KSZIirjK9G1fhVPLWwfdBhgO7F/JvxFfhQ2sxkS2Cpk1w\nSWLHQt52wYgex1fcFYl2beCe6L9VakjDEcCbhethwYymARNxae1O4FNR3g/YtMQ+WSE+R+CHrA7C\n3RvGAjtFWdc6P8edcW3H1bhF7g+CpiYZY1x3Y0FJu2aLCFziG49L1INwTcNVwYSejjHbpIR2rwUu\nL1wLX1RdHuNzdOR3wSXV6cDQeo5Vpqoxa20CMi0kde2AR/j4Pb4n9jZwMaHyAXbH9yEG14GuK3HV\nX2US2x/4DR4p5mXcOu7UQv1rgd4l0VKZ+LfBLUorjO87MdmOwy0Wf40vHLaP8k9WmFML298SODG+\nr4kvXJ7HJY13gQsINRu+MHiNFqrdFkPPOhQWBpG3RozLAbgV7sR4jr5QqFPagqUJGlcm1Nu479/I\nJhhjkQkuVwadld8F/gL8Cl/snVUo74zvx/6+hD64GdcSVKTCMThznhpl8yvvEL4Pv0+9xidTM2PW\n2gR09ISvos/HDTZGxIpxLxp9t3akoE4BvojvZ5XCfKpoOx632DwL37t8NRjl53HjgB8ELWXvSw0M\nhvuJuL47mM6zFcYM9I2ytXGp+qQatr8lbqBzQVx3CpquwFXa2+CWuZX6/aNftimpPzbE3W5ewxct\n2xHaAlyKvTW+b4WrUm+nINWXPFaHAavG9wrjq+yfVRjjw8C5kdcbuKRsWuL6S/h2w7+AkyOvS3zu\ni0tpa7eUIVc9C0fglr73RbsT8W2GyrN8cZT3qPqNui1eMi2Y0vq0FSE/VuhefJXaC9/P+Bwwwszu\nqviaVd02BJfUZpdAT6/4/fWA35rZ5eGH95WocgjwsIXfoaRZ+Eq3Rabqi6FpS3wSvdzM3gYwsz0l\nHYobi4wzs+lVt/0bd8WoVfsTgMvM7Ixofz7uUjC5Gd+1g3DL2xm1oKEJbIYvVqYBGwEnA5+SdCnQ\nAGwsaTszmyBpOC7dD5M03sxKi5crjx96LXCSpO3NbLY8wtC8eJbfkHQRblW6i6QewG6UEKO2KVrw\n/noA+Da+B4wt6I7SALxvwZWWst0RwOfivbnNzEZJmoPbBdyL+86+W3hmPsDV/gtYQ7eEhkQL0dpc\nuaMmoA+udjuD2FvBjQ3eIlQoLKhO6oWvst+kYHFZQ3q2xBnJk/iL+jpweJStTGH1W7jnMlwKadJi\nrkY0zQEu/Bj3nIerNterQftbRH9fVJW/G+54Dwuq/zbAV/6zyxijKhr2xyf4K3CrxkPif4/CFyp3\n0SgF9cH9F8t+pvfF9+aei2dpjcivqC8rEuMn8LCF84FfF+6vpcq0mpYehTEdHW2PxqXp7YFHgGta\n2OZl+ALxOmAKzmQvoBkjONxKehJwTtljk+ljjGNrE9ARE77382y8iEUjlpVwS7Wjq+qfjVtSPgFs\nWQI9/YL5nIWrj7rjhiINwLpRpzj5r4JbpL5GqC1LoGnDYM7nx3VlQj0W2K+J+gNxo4nXgAE1aF/R\n5+/h6uKKKvBMXA22eVX9I3AH68fLGKNCO50L34fhUvSYGLd1cNX7/YSxBnU05Qc2D0b0PXyx9CKw\nepRVL/BmArcU8mpKZxO0vFRgjBvgLk0z4hl/Gri5OPZL0d5FuEp740Ler6LdDYu/i1tQDw7GeXdL\n2s1U+9TqBHTUhFu+TcBNsdeMvP7BCPasqrs37mO1fgl0fApfNd9UlT8wGOUuVfkX4H5w02vBfJqh\nSbgV7izgqkL+6bgE+7mq+t/B1ZnjqSGTxiPhjMPdCQbFBNtA1f4crsbdDpfYWiyhNkFHReKqTKrF\nhdTQ+O//A/Sp1/NbRV/RUOxU3Ep6SPTbjGrGGM/+n5u6v2RaitJrJzyYfX/C6nRpacGN3+YDP6jK\n3zGelwGFvHXxxdZ9wOgy+iBTC5+h1iagIydc3fIo7rA8IF7aKwrlxRe8NN8yfMU6FV+9rhh5Q4Ip\nblNV92g8Skyphj64yvbIoO3HMcE1AF9upv4QYnHRwnZ7BqM5CvcfWyMm1pdwVepuUW+hVX1TeTWg\npzduTXsFLqV2a6LO0HiOxlDSQqUZ2g7DVdw9CnkDcYvXrYP2iUXGGHU6NfW9TrTMLNJSi/HDNSe/\nxCX042j0Xf1R/PdVq+rvi8cLrmkfZKpNanUCOkqKyXb/eCG2KuT/EFe5vQWMKeSX7egtFpQ4Hsb3\nXzaPCeQV4NJm7l2hJJrWxA2NhuAq3OWCOU3FV+JfjHpFFWLNFgv43tvjuM/lxTSqbFfBD+qdhlv/\nVvJLV3fhTubz8f3kG3E3kOMJl5NCvWG4u8OtlKTSrmrvsKDrGXxv7vhC2Q3A2Pi+OfAQ8AIlWVjW\ngpYWtF1ZRHbH90kn4ouUM3CtxqAo79TUO12PZyjTxxzT1iagIyR8z24Gvqn+f7gRxKcL5RfEZHcW\njebspTFF/FijK3GXj+8V8ifhEtErFIwOymbQhT56Jhjz/GBCn8EDNR+DS4zXFurXVHIOhvg6bqiz\nciH/q7jvaDdcPTsBV5fVkzFeQaMv26m4Q3wDvo/1lUK9obhabt2S6RHu3jA16BiO7x/eiau4t8LV\nzlsXxvZZ3IK3XdACHIgvaB/GI+McEIxvdDzD7xJaDUqINpWpvNTqBLT3hDtRV0KldQe+HExn26p6\nP8INb75P7HuURM+WMXncjhsCzK1ijPfQGL2/LqtYfF9nDu73uBUuUb+BOzcLV6Uei0txPyvcVxPG\niBs+3EchvFjknxp9cR/w2Ri/cbjV5z517J/TgIeq8ibHOE7GDXz2wSXruoQIwx3ed4pn++e4mnl4\nPD+zo9+OLtTv2V5owbcPZsTz+TNcgzAvGGIP4Ce4n+oRVFneZmr7qdUJaO8pXoxxLGi9+fvIH0ao\nBCP/klhlnlLGSxTM510WdEK/MhhyUToah6uYtq8V41kETRvjUXtGVeWfERPa+nG9UjDGRyiY8deI\nhs1xw6EhNEqAI/AFw1Exuf4JN6bphkutfyiDAeGGT9/AJY9tCvnTgFPi+8/xvbGdcAOgB4Omtevw\nPC+wHwjsjFv83lTIH4arnxeih9q6XdSdFvwki1fwgA0Vhrde5L+PR1VSMMu/xfOz/MdtJ1PrpVYn\noL0n3DLyOWIfMSb7+XiszIm4ef/wQv3zKMGnLF7cVymYwUf+zXjwgKdx5+I9I388hT2REvtnNxr3\nzIpWgIfhKuWNaLS6XAlfMNwPrFNDGg7GV/rFhUtPIowa7uh9Ly6VfRKXLDcooS/6x7MyFWfIT9Ho\nWvFfMdH+rjIpV937yZLHaRBNhMsLBrAzLrUW3QsqDKMM46O60xK/3R1fIB1XyKs8m6vEGM2N56k7\nHoD8WQph9jK1/dTqBLT3hPvbPRQvx63BAPaOF2pNPDDwOCJKf4l0bBBM+E5gh8g7DVdbfj+Y0NO4\nWqhXlN9Lwe+qxvR8El9tr4PvCb2ES62rRtm/gfMK9YuMcbUa0zIYX+XvW2wrvlckx8Oj/0pRA9Ko\nQr4YN9vfHY/VORl38t4SVynPpsCQqcOJF7i0Mx/fqxvOwqr/5YIZ/Qs/33OBMWsvtOBS/Bs07hVW\nn6KxbozXr+J6NTw6Vanjk6nGz1hrE9ARUjDG/XEn/N9UlZ2K75WVfhYhfsLEH4Ix/jQmji8VynvF\nhFPTo5aaoGMLXOV3Dx5ODuCbMdGNYWH/xIWCR9eYnp7RF3fSjC8ofsDsLRSO9Kph+81J8YcHo9wi\nrk/C9zNrJiUvIX2H4n6Q38Tjzj6Eq3D70BjouhOuzm0AHmiPtOCReBqA05soqzyj5+FRobo2VZ6p\n7adOJEqHmb1gZrfg0lBXSSsUitfCpbPOdaDjWdycvytuqTjSzO6RY3n89I0puIUsklRrGiT1wSey\n+/CV/v5B24245LoXrra9skC3FT9rDTN7CZdAdgPOk7RFgd6VJY3E42WeYxF/tcbojO/hdpE0uJA/\nA98DXj6up+CGHH1LoGFRuB83EPsXfijwCbgKeRRwi6RB+J70fTizmtJOaTF8L3cPSb0rmVXvyWrA\nBDN7T9JHsaXLenYTJaC1uXJHSriE9Aa+4j+YxjiZ/epMR2/ccGQsC55LeC6+j1fzqCzx+6vjks7l\nVfnFEGD744uHH1OS6rYZ2jrj+7//wdXIo/GA0nfje3hbldx+RYr/E274sxIulVxcVe9B4ME69Een\nqs9jcReennG9afTVVPy4qt8RhkCF36iVH2JbomVItPVzqk6HwbdDno53/HHgu9T5HMtMNRjj1iag\no6V4qabjcRnHUXLg6EXQUZmE/4i7QZyCx/ksbfKPRcF03EG/U1VZ0WhhKL4iv7564qlDvwwCbotJ\n7QHclaYuzDnGZCyNRk4/KpRVzpPcoR70ULVXicd/fRLfN1sNl9TGRNlXYgFxY3unJdo4Cjeo+Wsw\n6L7A14B/xDt9APB1SrYTyFROqkxCiTpC0uq4SuwDM3ujFenYBA81ty0+uWxnZo+W2N5B+H7QCmZm\nTR2NJalb0LINzpCGmNm/yqKpGTqbOg6qXm1vQhzWDAwzs/sjv6ljxMpo/0C87wfjAegnm9nVUTYG\nXzSsibsVHWVmc6Ksi8UxTJJkNZhY2hItVXRVAgb8GN+P7oq7Cj1uZiNq2Vai/kim2MEh6dO4O8Tp\nZja15La2xy0qDzaz25qpcyxuBTpE0spm9laZNDVDw0cTaRmT6hK0vzG+pyrcAvehOrV7CS7h/B0/\nD3JHPPjEPXhwgL64K9FYQtVc3Tc1ZIhthpZF0Lga7re6JvCymTVEfqstqhItRx4y3MFhZtMkfc3M\n/lOH5mbiMV6HSXrEzGbCQpPXBsCUMFIow6hlsShOpPVmiNHmdEnH4VL8pZJOMLO/l9mmpBPxfe49\ncYlnnqT1cMZ0Lu5m8A1Jj+PnZ86N+1Tr/mpLtCwK5gcXz8b3MSu0Kxniso20Pk1QJ4aImb2Mn3yx\nKwUrz1CldpN0IW5ReLWZzWsNhtRWYG4pfDJudDSrrHbC8rg7bnl7kZk9AnwYk/uLuMHR94Gvhvr7\n+8DOkvYKOms2Rm2JlqVFW6Ah0TIkU0zUG3fgbiEHArdJul7S1Xgc1sOAr5rZtNYksK3AzJ7BI9r8\ns8Q2DA+YsC0eYKKYj5m9iftnPokHFHgGl+B7tmdaEh0XyRQTdYWZzTez63Aryidxy9e+uCn7YDN7\nrDXpa2uoqAZLxlu4NeVW0eZH0k5IabNwY5YB5n6ah1QMXto5LYkOiNxTTLQKzGyipANy/6VNoOiU\n/mszew6adEp/OL5PiPIyLGLbEi2JDoiUFBOtiY8msTKi5ySWDGb2Du6nui1wpqSNIt9iv3dN/LDj\n/cK45ThJXctgQm2JlkTHRLpkJBIJACQdhfvePYiftzkO2Aw4Ew8mcB0eCvB+K9l3tC3RkuhYSKaY\nSCSAtuWU3pZoSXQsJFNMJBILoC05pbclWhIdA8kUE4nEYtEakX2aQ1uiJdH+kEwxkUgkEolAWp8m\nEolEIhFIpphIJBKJRCCZYiKRSCQSgWSKiUQikUgEkikmEolEIhFIpphIJBKJRCCZYiKRSCQSgWSK\niUQikUgEkikmEouBpPUlzZfUP653kvShpJVbgZZxki5rwf0vSDquljQlEu0JyRQTyyQkjQlG9aGk\nDyQ9K+lMSWU908XQTw8B65jZW0tyY0sZWSKRqB/ykOHEsow/AIcAKwJfBq4GPgBGVlcMZmktiJn5\n0XmPZjYPaFjK30kkEm0YKSkmlmV8YGavmtmLZjYKuBfYG0DSIZJmS9pT0lTgfWC9KBsu6SlJ78Xn\nkcUflbStpMlRPhHYioKkGOrT+UX1qaQdQiKcI+l1SX+QtIqkMcBOwPEFybZX3NNX0lhJb0v6P0k3\nSFqj8JvdIu9tSS9LOnFJOiX+88Sg/1VJty2i7gmSpkh6R9I/Jf1EUvdCeS9Jd8V/ekfSE5J2i7JV\nJd0kqUHSu5KmSfrWktCYSLRVJFNMtCe8D6wQ3w0/cugU4DCgD9AgaShwNvA9/NDa04FzJX0TIBjC\n3cCTwMCoe2kTbRWZ5ACcIT8JfBbYDrgT6AwcD0wAfgqsBawDvChpFeAvwKPRzq748Ui3FNq4FNgR\n2BM/W3DnqNssJO0B/Bb4HTAg7vn7Im75EDgW2AIYBgwBLi6UX4336WCgL3Aq8E6UnY/34a7xeSTw\n70XRl0i0daT6NNEuIGkXfHK+vJC9HHCkmT1ZqHc28F0zuzOyZkrqA3wHuBEYiqtKh5vZXOBpSevh\nzKE5nAxMMrNjC3nTCm3OBd41s1cLeccAk83szELecOCfkjYGXgG+DRxkZuOj/FvAS4vpitOBX5rZ\nuYW8qc1VNrMrCpf/lHQmcA1wTOStB9xqZk/F9YxC/fWAx8zsscr9i6EtkWjzSKaYWJaxp6S3geVx\nRnYTcE6hfG4VQ+wG9AZGS/pZod5ywOz4vhkwJRhiBRMWQ8cAFpTwlgRbAp8P+ouwoLEb/r8mflRg\nNlvSNBaNAcCoJSUiFhOn4f97Zbwvukha0czeB64ArpG0Ky4N32ZmT8Tt1wC3SdoauAe4w8wW11eJ\nRJtGqk8TyzL+CvQHNga6mtm3zey9Qvl7VfVXis/hOFOqpD64ynNpUd3OkmAl4C6c/iItmwD314MW\nSevjquLHgX1x1ezRUbwCgJmNBjYEbsDVp5MkHR1lfwR6AZfhauF7JS1k5JRILEtIpphYljHHzF4w\ns5fMbP7iKptZAzAL6G1mz1elmVHtaaC/pBUKty6OYU4BvrCI8rn4/mIRk3FmPLMJWt4DngPmAYMq\nN0haDdi0hbQUsTV+0PhJZjbRzKYDn6quZGYvm9koM/sazgAPL5S9ZmY3mtkw4ATgiCVsO5Fok0im\nmOhoOAv4nqRjJW0SFqCHSDohyn+JqzB/JmlzSbsD323id1T4fhHwmbDc7CdpM0kjJK0e5TOAQREE\noGJd+hNgdeBmSdtI2kjSrpKulyQzmwOMBi6RNERSX2AMbhizKJwDHCjp7KCjn6RTmqk7HVhe0nGS\nNgxjo+8s8CelH0n6kqQNJA3EDXGeirJzJO0lqXfsy36lUpZILKtIppjoUAh14HDgUFyqGg98C3g+\nyufg1p59cWnuPNyCdaGfKvzms7h1aH/gYdy5fy9c0gO3Iv0QZxgNknqZ2SvADvg7+Keg5TJgdsGX\n8mTgAVzNek98f3Qx/+8+4OvxHx7D9wE/0wzdU4AT4/89ARyI7y8W0Rm4KmgfCzxDo4p1LnAh8A+8\nH+fFbyQSyyy09L7MiUQikUi0L6SkmEgkEolEIJliIpFIJBKBZIqJRCKRSASSKSYSiUQiEUimmEgk\nEolEIJliIpFIJBKBZIqJRCKRSASSKSYSiUQiEUimmEgkEolEIJliIpFIJBKBZIqJRCKRSAT+H/QE\n8klBH4DmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8848dbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_tree3.pkl']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(rand_search_cv, \"decision_tree3.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "rand_search_cv = joblib.load(\"decision_tree3.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
